




































Indian Journal of Finance and Banking 

 Vol. 5, No. 2; 2021 

                                       ISSN 2574-6081   E-ISSN 2574-609X 

Published by CRIBFB, USA 

 

115 

INDIAN IT FIRMS CREATING MANAGEMENT CODES FOR 

FOREIGN EXCHANGE RISK 
 

Dr. Nitin Shankar 

Assistant Professor 

Amity Business School, Amity University, Uttar Pradesh, Lucknow Campus, India 

E-mail: nitinshankar19@gmail.com 

 

Dr. Fatima Beena 

Associate Professor 

American College of Dubai, United Arab Emirates 

E-mail: fatimabeena@gmail.com 

 

ABSTRACT 

Purpose: India has been a preferred I.T. service sourcing nation globally and has been 

registering high growth. India has a significant pie of the global sourcing market, accounting for 

nearly 55 % share. It covers significant global through its more than one thousand centres 

spread across continents. With a year-on-year growth of 6.1%, India’s I.T. and ITES industry 

will increase to the U.S. $ 350 million by 2025. The extensive expanse of geographical coverage 

also translates into foreign exchange risk; hence foreign exchange risk management becomes an 

important strategy. The current study attempts to assess the impact of foreign exchange risk 

management on the Indian sector over 2007-2017; the period includes the 2008 financial crisis 

taken up in the current study. 

Design/Methodology/ Approach: We analyzed the Indian I.T. companies listed on the BSE Ltd 

on their exposure, approach, and management towards foreign exchange risk. We investigated 

their annual reports from 2007 -2017 to understand their exposure and the adopted external 

foreign exchange risk management techniques. We further assessed the impact of these foreign 

exchange risk management techniques on the firm’s value. 

Findings: The impact of foreign exchange risk management was significant on small-cap I.T. 

companies for the study period. Though for the during the 2008 crisis term, it was found to be 

insignificant. 

Practical/Implications: Foreign exchange risk management is crucial for Indian I.T. companies 

indulging in cross-border trade. The current study incorporates external methods of managing 

foreign exchange risk management; hence even if the impact were found to be insignificant for 

Mid Cap and some Large-cap companies, they would be practicing internal hedging methods, 

which puts a strong case tapping trillion-dollar business through a fully functional competitive 

International Financial Centre. 

Originality/Value: Our paper contributes to the literature on Foreign exchange risk 

management by Indian I.T. companies, which contributes handsomely to India’s GDP and 

Foreign exchange reserve. 

 

Keywords: Foreign Exchange Risk Management, I.T., BSE.  

 

JEL Classification Codes: F31, G32. 

mailto:nitinshankar19@gmail.com
mailto:fatimabeena@gmail.com


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INTRODUCTION 

The objective of this current study will be to investigate foreign exchange exposure of Indian I.T. 

(Information Technology) Sector Companies, its measurement, and the steps taken to manage it. 

In particular, the paper would focus on measuring foreign exchange exposure of Indian of 

pharmaceutical Sector, External Control techniques to Manage Foreign Exchange Risk & Impact 

of the Internal Control techniques to manage Foreign Exchange Risk. Risk exists whenever real 

outcomes deviate from the desired outcomes (Butler, 2002). Hence, the present study's risk is the 

possible event where outcomes are uncertain, leading to losses for the corporation involved. 

(Jorion, 2007) Business and Risk are not unknown to each other. Risk has been defined in 

various ways. 

The uncertainty in the value of an asset, equity, or earnings due to unfavourable 

movement in the exchange rate is known as risk. As per Dun & Bradstreet, Risk is defined as any 

possible event that can hinder the corporation's current and future reading. 

Foreign exchange risk is also one of the multiple risks faced by the companies; however, 

for companies exposed to cross border trading, i.e. foreign currency, it becomes one of the 

critical factors affecting the firm‟s performance. The amount of trade in which firms engage in 

cross-border trade is exposure, and the volatility related to foreign exchange is a risk. Hence 

exposure and risk both are pretty different concepts (Levi, 2009). 

It becomes an essential block in the firm's financial management if its objective is to 

minimize losses or maximize gains. With the current government's initiatives to increase cross-

border trade through various initiatives like 'Make in India' to boost our economy, the integration 

of our economy with that of the world will become more intense and increase our firm's 

exposure multi-fold. 

The amount of trade in which firms engage in cross-border trade is exposure, and the 

volatility related to foreign exchange is a risk. Hence exposure and risk both are pretty different 

concepts (Levi, 2009). 

Pramborg (2005), Firmwide risk management is a popular term representing a combined 

and harmonized risk management outlook. Other expressions utilized to explain this combined 

and coordinated risk management are intercontinental risk management, strategic risk 

management, and enterprise risk management. The exchange risk exposure on both short and 

long term on a global basis is addressed by Firmwide risk management. 

Muller and Verschoor (2006), from the survey of Nine hundred thirty-five U.S firms, 

twenty-nine percent of firms significantly impacted the foreign exchange currency movement/ 

fluctuations between 1990 and 2001. 

Meier (2000), Firmwide risk management is one of the most widely used out of a group 

of synonyms that describe a broad and comprehensive view of managing risk across the firm.  

Other terms used to describe this type of coordinated risk management are enterprise risk 

management, global risk management, and strategic risk management. Firmwide risk 

management addresses exposures globally in that it considers all parts of the firm and tries to 

cope with both short-term and long-term exposures.  For example, The Tower Group uses the 

term enterprise risk management. It defines it as “the process of managing the risk faced by an 

institution on a global, institution-wide basis.” 

Marshall (2000), a high proportion of U.K. respondents ranked foreign exchange risk 

management as significantly essential or most important, which is expected as the U.K. MNCs 

rely on a high degree of overseas business. One U.K. respondent explained that foreign exchange 

risk „„impacts directly on the creation of shareholder value and competitive position‟‟. However, 



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this view does not seem to be shared in the USA as 45% of USA respondents report that foreign 

exchange risk management is marginally essential or least important. One USA respondent 

explained that „„foreign exchange risk management is another attribute of doing business 

globally. It is one of the many items necessary to do business but is no more a burden than 

another item‟. 

It is a pearl of market wisdom that a firm's cash flow and valuation are affected by 

exchange rate movements, i.e. firms are exposed to foreign exchange exposure. Tracking foreign 

exchange exposure and devising its management strategies has garnered considerable attention, 

resulting in much research. For firms operating out of bustling economies like India, foreign 

exchange risk management becomes imperative  

Gendreau (1994) finds it difficult and unconvincing that the weak results imply that 

exchange rate changes do not affect exporters‟ stock returns.  

Bartov and Bodnar (1994) attribute the observed insignificant relationship between 

exchange-rate changes and stock returns to potential problems associated with the previous 

studies‟ sample selection procedure or mispricing caused by investors‟ errors in estimating this 

linkage.  

Jorion (1990) finds that dollar depreciation exposure is positively related to the ratio of a 

firm‟s foreign sales to total sales. 

However, these studies were based on an economy whose exchange rate is very stable. So 

it has called for more to study this phenomenon in the Indian context. The study takes on the 

Indian I.T. sector as its testing ground. Being a sector that developed and flourished on business 

from developed countries and bought foreign exchange in tonnes for the country, this is a perfect 

sector to undertake this study. 

The exposure we are trying to measure is transaction risk which can be mitigated at a 

firm level, unlike economic exposure. Rodriguez (1974) states that proponents of the transaction 

exposure definition argue that a foreign operation is a long-term proposition. The only relevant 

exchange risks are those involved in short-term fluctuations in the host country's currency and 

other currencies. 

Al-Momani & Gharaibeh (2008), transaction exposure is related to the risk that arises 

from day-to-day transactions dealt with foreign currencies subject to volatility in value against 

the local currency. 

External techniques are used by both exporters and importers as well as by multinational 

companies. The costs of the external exposure management methods are fixed and 

predetermined. The main external exposure management techniques are forward exchange 

contracts, short-term borrowing, discounting, forfeiting & government exchange risk guarantees. 

Dufey & Srinivasulu (1983), Hedging can be accomplished either by forwarding 

contracts or by foreign currency borrowing and lending. The former is known as a forward 

market hedge and the latter as a money market hedge. Hence, obstacles to individuals in foreign 

money markets are also, in essence, obstacles to investor hedging. In many foreign money 

markets, nonresidents are denied access to local borrowing facilities or face discriminatory taxes. 

Joseph (2000), a firm's degree of internationalization can affect the extent to which it uses 

hedging techniques (Mathur, 1985). Since firms appear to initially use internal techniques to 

hedge exposure (Hakkarainen et al., 1998), a positive relationship is expected between the 

measures of internationalization and the degree of utilization of internal techniques. In contrast, a 

negative relationship is expected between the rate of utilization of external techniques and the 



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internationalization measures since the greater use of internal techniques implies less use of 

external techniques. 

Joseph (2000), in general, external techniques appear to play a much more important role 

in hedging decisions than internal techniques. As the firms are large, scale economies in the use 

of external techniques and the availability of skilled treasury personnel may contribute to their 

greater use. 

Carter, Pantzalis, and Simkins (2001), the use of currency forwards and other derivatives, 

decrease the firm‟s foreign-exchange exposure.  Neither foreign-exchange options nor swaps 

appear to be associated with a reduction, or an increase, in exposure (In context to 

U.S. Multinational Corporations). 

El-Masry (2006), large-sized firms are more likely to use derivatives because of the 

economies-to-scale argument for derivative use. Large firms are better able to bear the fixed cost 

of derivatives uses compared to small firms. 

El-Masry (2006), it is interesting in knowing, if a firm uses derivatives for hedging, the 

most important reasons for using derivatives for hedging purposes. Four reasons for hedging are 

identified, and firms are asked to indicate the importance of these aspects. It was found that the 

most important reason for using hedging with derivatives is to manage the volatility in cash 

flows at 37% of the responding firms. The firm's market value is considered the second most 

important reason for using derivatives for hedging purposes, with 29% of the responding firms. 

This is followed by managing the volatility in accounting earnings at 25% and managing balance 

sheet accounts or ratios at 19%. 

Baranauskas, Jonuška, and Samėnaitė (2003), the main reason why forwards preferred 

options was that it costs less to use forwards. The majority of derivative users claimed that they 

are not willing to pay option premiums. 

Rupeika-Apoga (2005), recently, most firms have adopted a more comprehensive 

approach to foreign exchange risk management, sometimes motivated by poor results of active 

foreign exchange management 

 

RESEARCH METHODOLOGY 

Researchers believe that foreign exchange risk management is essential for the firms exposed to 

cross-border trade, as it may directly impact the firm‟s performance and profitability. Particularly 

in India, the research in this aspect is little, so the researcher wants to throw light on the 

exchange risk management's effectiveness. 

Thus, the present study used annual reports of listed companies across five sectors as The 

Companies Act, 2013 made it compulsory to disclose foreign Exchange Earnings/ Outgo and 

their Risk Management Policy. Further authenticity of the data made the study more robust. The 

firm‟s stock return data was taken from the stock exchange itself, i.e. BSE Ltd. Thus, the present 

study quantitative method is better. 

 

Validity and Reliability 

The present study data has been from the firm‟s annual reports taken from its official website and 

their returns on the stock exchange from BSE Ltd. The data was analyzed carefully with valid 

tools to ensure the present study's reliability. The present study period is for 2007- 2017, so the 

results will be changing if the year or period changes.  

 

 



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Data Collection Method 

The current study was being based on six listed Indian I.T. companies; two each from Large Cap, 

Mid Cap & Small Cap will be selected from the stock exchange (BSE). Risk Calculation through 

a model developed based on stock price and comparing it with base year price. 

The present study is done over ten years, from 2007 to 2017, to reflect the economic 

cycle in the findings. The periods also encompass one of the most turbulent times from 2007 to 

2010, thus making study more enjoyable. 

 

Sources of Data 

Data forms the bedrock and basis for any relevant and authentic research. The present study, in 

its endeavour to FERM policies and their impact on Indian I.T. firm‟s secondary data sources 

were utilized. 

Secondary data was mined from the company's authentic sources, Bombay Stock 

Exchange Ltd. (2018). (BSE Ltd.), Securities and Exchange Board of India (SEBI), Reserve 

Bank of India (RBI), World Economic Forum (WEF), World Trade Organisation. 

 

Tools & Techniques 

Regression-based impact analysis. The study will be structured in two steps: 

 

I Step: To study the foreign exchange exposure of Indian non-financial companies by 

scrutinising their financial statements and Annual Reports.  

II Step: Once the Risk has been established, we try to determine the methods/techniques adopted 

by the Indian non-financial companies for foreign exchange risk management.  

 

OBJECTIVE OF THE STUDY 

The main objective of this study is to investigate the relationship between foreign currency risk 

and international business involvement, legal structure, firm size, sector, and management 

practices in the Indian environment.  

The impact of firm-specific characteristics on the value of Indian IT companies will also 

be investigated by examining the level of foreign sales hedging, ROA, C.R., ERR 

 

PROBLEM 

There is a necessity to study the risk management methods adopted by the Indian I.T. companies 

to cover their exposure and its effectiveness to achieve the same in the absence of specialized 

financial services.  

(Shapiro, 1975; Hodder, 1982; Levi, 2009; Bodnar & Marston, 2002), whether a firm 

operates in the domestic or global market, it is affected by the exchange rate movement. Further, 

it is a standard theoretical view that exchange rate movements are one of the reasons for 

macroeconomic uncertainty 

 

MODEL 

The study evaluates the impact of foreign exchange on the firm value; it uses regression impact 

analysis. To make the model more comprehensive, three more independent variables were 

included in the model.  

 

  Y = a + b1X1 + b2X2 + b3X3 + b4X4 



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Y representing the firm's value and the independent variable is hedge foreign exposure, 

Current ratio, Return on assets ratio, Earnings Return Ratio. 

Anticipating the impact on a firm‟s value due to fluctuations in exchange rates has been a 

challenge for firms operating on a global scale. The risk management tools and techniques' 

effectiveness becomes the next challenge as different markets may demand different exchange 

risk management methods.              

The present study has two primary areas of focus and inquiry the value of a firm and 

exchange risk exposure.  

 
Figure 1. Conceptual Framework 

 

HYPOTHESIS 

Ho1. There is no impact of tools techniques used to manage foreign exchange risk Indian non-

financial companies. 

 

DATA ANALYSIS 

Table 1. IT TCS Co-efficient 

 

Coefficients 

Model 

Unstandardized 

Coefficients 

Standardized 

Coefficients t Sig. 

B Std. Error Beta 

1 
(Constant) 253193.569 241486.511 

 
1.048 0.335 

HCE_TCS 1.51 0.568 0.648 2.657 0.038 



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ROA_TCS 485.766 711.228 0.185 0.683 0.52 

CR_TCS 68187.365 176674.561 0.143 0.386 0.713 

ERR_CS -3136.109 2560.868 -0.212 -1.225 0.267 

2 

(Constant) 330900.813 124965.134 
 

2.648 0.033 

HCE_TCS 1.684 0.326 0.723 5.17 0.001 

ROA_TCS 710.037 384.363 0.271 1.847 0.107 

ERR_CS -2502.675 1842.435 -0.169 -1.358 0.217 

3 

(Constant) 172830.561 47892.19 
 

3.609 0.007 

HCE_TCS 1.668 0.342 0.716 4.872 0.001 

ROA_TCS 867.248 385.398 0.331 2.25 0.055 

Source: Above Table is Compiled by the scholar 

Table 2. I.T. Infosys Co-efficient 

 

Coefficients 

Model 

Unstandardized 

Coefficients 

Standardized 

Coefficients t Sig. 

B Std. Error Beta 

1 

(Constant) 1100142.737 190801.873 
 

5.766 0.001 

HCE_INFOSYS -0.319 0.411 -0.161 
-

0.777 
0.467 

ROA_INFOSYS -37685.422 7529.584 -0.893 
-

5.005 
0.002 

CR_INFOSYS 22383.187 40557.228 0.121 0.552 0.601 

ERR_INFOSYS 685.044 2584.716 0.052 0.265 0.8 

2 

(Constant) 1099284.614 177653.663 
 

6.188 0 

HCE_INFOSYS -0.371 0.337 -0.187 
-

1.101 
0.307 

ROA_INFOSYS -36702.056 6101.35 -0.869 
-

6.015 
0.001 

CR_INFOSYS 29162.843 29308.391 0.158 0.995 0.353 

3 

(Constant) 1216289.661 133085.264 
 

9.139 0 

HCE_INFOSYS -0.192 0.285 -0.097 
-

0.674 
0.519 

ROA_INFOSYS -37412.406 6055.687 -0.886 
-

6.178 
0 

4 
(Constant) 1228294.241 127832.811 

 
9.609 0 

ROA_INFOSYS -38925.188 5451.933 -0.922 -7.14 0 

Source: Above the table is compiled by the authors 

 

 

 

 



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Table 3.   IT Tata Elxsi Co-efficient 

 

Coefficients 

Model 

Unstandardized 

Coefficients 

Standardized 

Coefficients 
t Sig. 

B 
Std. 

Error 
Beta 

1 

(Constant) -3897.464 3387.637   -1.15 0.294 

HCE_TELX 0.751 0.833 0.239 0.902 0.402 

ROA_TELX -71.765 102.342 -0.231 -0.701 0.509 

CR_TELX 6587.028 2850.181 0.892 2.311 0.06 

ERR_TELX -27.757 69.564 -0.158 -0.399 0.704 

2 

(Constant) -4037.79 3160.513   -1.278 0.242 

HCE_TELX 0.727 0.779 0.231 0.933 0.382 

ROA_TELX -84.794 90.982 -0.273 -0.932 0.382 

CR_TELX 5924.369 2172.788 0.802 2.727 0.029 

3 
(Constant) -3946.699 3132.949   -1.26 0.243 

HCE_TELX 0.798 0.769 0.254 1.038 0.33 

Source: Above Table is compiled by the authors 

 

Table 4. I.T. Mind tree Co-efficient 

 

Coefficients 

Model 

Unstandardized 

Coefficients 

Standardized 

Coefficients t Sig. 

B Std. Error Beta 

1 

(Constant) -2959.707 3253.533   -0.91 0.398 

HCE_MTREE 0.495 0.515 0.305 0.963 0.373 

ROA_MTREE 8.085 56.83 0.034 0.142 0.892 

CR_MTREE -568.669 415.285 -0.304 -1.369 0.22 

ERR_MTREE 63.456 37.999 0.462 1.67 0.146 

2 

(Constant) -2938.613 3014.128   -0.975 0.362 

HCE_MTREE 0.455 0.399 0.281 1.14 0.292 

CR_MTREE -558.146 378.969 -0.299 -1.473 0.184 

ERR_MTREE 65.147 33.471 0.475 1.946 0.093 

3 

(Constant) -3975.841 2927.021   -1.358 0.211 

CR_MTREE -666.867 373.598 -0.357 -1.785 0.112 

ERR_MTREE 87.797 27.441 0.64 3.199 0.013 

4 
(Constant) -7572.36 2367.008   -3.199 0.011 

ERR_MTREE 110.707 27.041 0.807 4.094 0.003 

Source: Above Table is compiled by the authors 

 



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Table 5. I.T. Hexaware Co-efficient 

 

Coefficients 

Model 

Unstandardized 

Coefficients 

Standardized 

Coefficients 
t Sig. 

B 
Std. 

Error 
Beta 

1 

(Constant) 140.822 2794.133   0.05 0.961 

HCE_HEXA 5.572 1.155 0.949 4.825 0.003 

ROA_HEXA 39.618 110.255 0.087 0.359 0.732 

CR_HEXA -606.528 917.223 -0.077 -0.661 0.533 

ERR_HEXA 4.636 13.176 0.065 0.352 0.737 

2 

(Constant) 860.486 1780.52   0.483 0.644 

HCE_HEXA 5.545 1.078 0.945 5.146 0.001 

ROA_HEXA 18.176 85.942 0.04 0.211 0.839 

CR_HEXA -643.382 852.288 -0.081 -0.755 0.475 

3 

(Constant) 761.058 1611.544   0.472 0.649 

HCE_HEXA 5.733 0.575 0.977 9.967 0 

CR_HEXA -599.698 775.943 -0.076 -0.773 0.462 

4 
(Constant) -250.501 918.936   -0.273 0.791 

HCE_HEXA 5.635 0.548 0.96 10.277 0 

Source: Above the table is compiled by the authors 

 

Table 6.  IT NIIT Co-efficient 

 

Coefficients 

Model 

Unstandardized 

Coefficients 

Standardized 

Coefficients 
t Sig. 

B 
Std. 

Error 
Beta 

1 

(Constant) 70.467 335.111   0.21 0.84 

HCE_NIIT 0.694 0.126 0.809 5.492 0.002 

ROA_NIIT -17.104 18.807 -0.173 -0.909 0.398 

CR_NIIT 164.55 74.513 0.167 2.208 0.069 

ERR_NIIT 7.692 9.27 0.11 0.83 0.438 

2 

(Constant) 281.831 212.824   1.324 0.227 

HCE_NIIT 0.766 0.09 0.893 8.548 0 

ROA_NIIT -3.888 9.776 -0.039 -0.398 0.703 

CR_NIIT 164.596 72.835 0.167 2.26 0.058 

3 

(Constant) 213.713 119.52   1.788 0.112 

HCE_NIIT 0.793 0.055 0.925 14.482 0 

CR_NIIT 152.687 62.806 0.155 2.431 0.041 

Source: Above the table is compiled by the authors 



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RESULT & DISCUSSION 

Table 7.  Results 

 

I.T. (INFORMATION TECHNOLOGY) 

SECTOR   SECTOR   

CAP   LARGE   MID   SMALL   

FIRM NAME    TCS SIG Infosys SIG 
Tata 

elexi 
SIG 

Mind 

Tree 
SIG Hexa SIG NIIT SIG 

                            

  A                         

COEFFICIENT TABLE                      

Y = A+ 

B1X1+B2X2+B3X3+B4X4 

intercept 

𝛼 
253194   1100142   3897   -2960   140.82   70.5   

                          

B1, HCE 1.51 0.0 -0.319 0.5 0.75 0.4 0.495 0.4 5.572 0.0 0.69 0.0 

P 

VALUE 
                        

                          

B2, 

ROA 
485.766 0.5 -37685 0.0 

-

71.8 
0.5 8.085 0.9 39.618 0.7 -17 0.4 

P 

VALUE 
                        

                          

B3, CR 68187.4 0.7 22383 0.6 6587 0.1 
-

568.7 
0.2 -606.5 0.5 165 0.1 

  
P 

VALUE 
                        

                            

  B4, ERR                         

  
P 

VALUE 
-3136.1 0.3 685 0.8 27.8 0.7 63.46 0.1 4.636 0.7 7.69 0.4 

Source: Author‟s compilation 

 

The present study findings of the I.T. sector show a clear trend that (Slope) β, i.e. rate of return 

per unit hedged funds, of the small-cap firms, are significant.  

 

Table 8. Hypothesis assessment summary 

 

OBJECTIVE HYPOTHESIS 
SECTOR/  

COMPANY 

INDEPENDENT 

VARIABLE 
Significance 

Ho: Null Hypothesis 

Accepted/ Rejected 

         To 

study the 

impact 

analysis of 

tools and 

techniques 

used to 

manage 

Ho: There is no 

impact of tools 

techniques used to 

manage foreign 

exchange risk 

Indian non-

financial 

companies. 

IT/LARGE/TCS HCE Significant Rejected 



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foreign 

exchange risk 

Indian non-

financial 

companies. 

H1: There is an 

impact of tools 

techniques used to 

manage foreign 

exchange risk 

Indian non-

financial 

companies. 

IT/SMALL/HEXAWARE HCE Significant Rejected 

  IT/SMALL/NIIT HCE Significant Rejected 

 

Financial Crisis times  

India has been a preferred I.T. service sourcing nation in the world and has been registering high 

growth. India has a significant pie of the global sourcing market, accounting for nearly 55 % 

share. It covers significant global through its more than one thousand centres spread across 

continents. With a year-on-year growth of 6.1 %, India‟s I.T. and ITES industry will increase to 

the U.S. $ 350 million by 2025. 

 

 
Figure 2. Stock Returns (large) 

 

The above graph reflects a sluggish IT Large Cap movement of TCS and Infosys on BSE in the 

crisis years 2007-09 and only starts picking up at the inception of 2010. 

 

Hoβ0, The Slope term in the regression of returns on hedged forex exposure, is statistically 

insignificant. 

 

Ho for Large IT firms TCS and Infosys is significant at 5% level with P Values 0.025 and 0.027 

for 2007-2010. 

 



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Though during the period of the present study, i.e. 2007-2017, The Ho only holds for TCS and 

not for Infosys 

 
Figure 3. Stock Returns (mid) 

 

The above graph clearly reflects the IT MID Cap stocks movement of Tata Elxsi and Mindtree 

on BSE in the crisis years 2007-09 and only starts looking up at the inception of 2010 and then 

dips in 2010 again. 

 

Hoβ0, The Slope term in the regression of returns on hedged forex exposure, is statistically 

insignificant. 

 

Ho for IT MID Cap companies Tata Elxsi and Mindtree is found to be is insignificant at 5% level 

with P Values 0.428 and 0.73 for the period 2007-2010. 

 

During the present study period, i.e. 2007-2017, The Ho is also insignificant for both Mid Cap 

firms. 

 
Figure 4. Stock Returns (small) 



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The above graph reflects a very rocky stock's movement of Hexaware and NIIT on BSE in the 

crisis years 2007-09, with business picking up in 2009. 

 

Hoβ0, The Slope term in the regression of returns on hedged forex exposure, is statistically 

insignificant. 

 

Ho for IT Small Cap companies Hexaware significant at 5% with P-value 0.046 and NIIT is 

found to be is insignificant at 5% level with P Values 0.238 for the period 2007-2010. 

 

During the present study period, i.e. 2007-2017, The Ho is significant for small-cap firms. 

 

CONCLUSION 

The study concludes foreign exchange management has a relationship with firms‟ stock 

performance on BSE; however, this impact varies with the size of the company in the Indian I.T. 

sector. The impact of foreign exchange risk management was significant for small-cap I.T. 

companies, Hexaware and NIIT, and large-cap companies, TCS. The current study undertakes 

only external hedging techniques as the same is available in the public domain. The firm also 

undertakes internal hedging practices like swapping, netting, etc. It also opens the opportunity to 

establish a sophisticated financial centre on the standard playing field as with other established 

international financial centres. The foreign exchange risk is imperative when a firm adopts an 

internationalization strategy to grow; hence its management also impacts its value, as found by 

the current study.  

 

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